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Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets

Domke, Justin

Description

Inference is typically intractable in high-treewidth undirected graphical models, making maximum likelihood learning a challenge. One way to overcome this is to restrict parameters to a tractable set, most typically the set of tree-structured parameters. This paper explores an alternative notion of a tractable set, namely a set of "fast-mixing parameters" where Markov chain Monte Carlo (MCMC) inference can be guaranteed to quickly converge to the stationary distribution. While it is common in...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
URI: http://hdl.handle.net/1885/103830
Source: Reflection, Refraction and Hamiltonian Monte Carlo

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